Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

Noise-Assisted MEMD-Based Phase-Connectivity Analysis to Personalize Closed-Loop DBS Therapy in Epilepsy Patients

Deep brain stimulation (DBS) is a treatment that has been explored for controlling seizures in patients with intractable epilepsy. Many clinical and pre-clinical studies using DBS therapy determine stimulation parameters through trial and error. The same stimulation parameters are often applied to the whole cohort, which consequently produces mixed results of responders and non-responders. In this paper, an adaptive non-linear analytical methodology is proposed to extract stimulation frequency and location(s) from endogenous brain dynamics of epilepsy patients, using phase-synchrony and phase-connectivity analysis, as seizures evolve. The proposed analytical method was applied to seizures recorded using depth electrodes implanted in hippocampus and amygdala in three patients. A reduction in phase-synchrony was observed in all patients around seizure onset. However, phase-synchrony started to gradually increase from mid-ictal and achieved its maximum level at seizure termination. This result suggests that hyper-synchronization of the epileptic network may be a crucial mechanism by which the brain naturally terminates seizure. Stimulation frequency and locations that matched the network phase-synchrony at seizure termination were extracted using phase-connectivity analysis. One patient with temporal lobe epilepsy (TLE) had a stimulation frequency ∼15 Hz with the stimulation locations confined to the hippocampus. The other two patients with extra-temporal lobe epilepsy (ETE) had stimulation frequency ∼90 Hz with at least one stimulation location outside of hippocampus. These results suggest that DBS parameters should vary based on the patient’s underlying pathology. The proposed methodology provides an algorithm for tuning DBS parameters for individual patients in an effort to increase the clinical efficacy of the therapy.